Christopher Lynn
Assistant ProfessorCards
About
Research
Publications
2026
Simple input–output dependencies explain neuronal activity
Lynn C. Simple input–output dependencies explain neuronal activity. Nature Physics 2026, 1-8. PMID: 42370308, PMCID: PMC13309188, DOI: 10.1038/s41567-026-03306-3.Peer-Reviewed Original ResearchInput-output dependenciesArtificial neuronsHigher-order dependenciesNeural networkNeural computationFeatures of synaptic connectivityNeural codingSummation of inputsCombination of inputsRobust to perturbationsLinear summationNeuronal activityInputSynaptic connectionsBiophysical detailsMinimal modelArtificial modelsComplex functionsComputerNetworkCodeDependenceNeuronsBrain regionsModelQuantifying the compressibility of the human brain
Weaver N, Faskowitz J, Betzel R, Lynn C. Quantifying the compressibility of the human brain. Proceedings Of The National Academy Of Sciences Of The United States Of America 2026, 123: e2531115123. PMID: 41564127, PMCID: PMC12846795, DOI: 10.1073/pnas.2531115123.Peer-Reviewed Original ResearchThis study investigates the compressibility of human brain activity, showing that only a small subset of neural correlations accurately predicts large-scale patterns, suggesting simpler brain dynamics.
2025
Minimax entropy: The statistical physics of optimal models
Carcamo D, Weaver N, Dixit P, Lynn C. Minimax entropy: The statistical physics of optimal models. Physical Review E 2025, 112: 061001. PMID: 41560230, PMCID: PMC13220842, DOI: 10.1103/kr9x-q59y.Peer-Reviewed Original ResearchOptimal compressionStatistical physicsMinimax entropy principleHigh-dimensional datasetsDescription length principleModels of biological networksMachine learningOptimal featuresNaive implementationOptimization modelMaximum entropy modelMinimum entropyLarge-scale experimentsComputational techniquesEntropy principleEntropy modelBiological networksFeaturesDatasetNetworkStatistical physics of large-scale neural activity with loops
Carcamo D, Lynn C. Statistical physics of large-scale neural activity with loops. Proceedings Of The National Academy Of Sciences Of The United States Of America 2025, 122: e2426926122. PMID: 41060767, PMCID: PMC12541312, DOI: 10.1073/pnas.2426926122.Peer-Reviewed Original ResearchThis study demonstrates a new statistical framework for modeling large-scale neural activity with feedback loops, capturing more information than existing methods and enabling better understanding of brain function.Exact minimax entropy models of large-scale neuronal activity
Lynn C, Yu Q, Pang R, Palmer S, Bialek W. Exact minimax entropy models of large-scale neuronal activity. Physical Review E 2025, 111: 054411. PMID: 40533950, DOI: 10.1103/physreve.111.054411.Peer-Reviewed Original ResearchExactly solvable statistical physics models for large neuronal populations
Lynn C, Yu Q, Pang R, Bialek W, Palmer S. Exactly solvable statistical physics models for large neuronal populations. Physical Review Research 2025, 7: l022039. DOI: 10.1103/physrevresearch.7.l022039.Peer-Reviewed Original ResearchNon-equilibrium whole-brain dynamics arise from pairwise interactions
Geli S, Lynn C, Kringelbach M, Deco G, Perl Y. Non-equilibrium whole-brain dynamics arise from pairwise interactions. Cell Reports Physical Science 2025, 6: 102464. DOI: 10.1016/j.xcrp.2025.102464.Peer-Reviewed Original ResearchNon-equilibrium dynamicsEntropy productionNon-equilibriumWhole-brain dynamicsNon-equilibrium processesPairs of brain regionsPairwise interactionsMacroscopic brain regionsWhole-brain scaleBrain dynamicsBrain regionsBrain statesEntropyBrain scaleBrain activityDynamicsComplex systemsHuman brainOrderInteractionDependenceRegionBrainScaleStateLSD flattens the hierarchy of directed information flow in fast whole-brain dynamics
Shinozuka K, Tewarie P, Luppi A, Lynn C, Roseman L, Muthukumaraswamy S, Nutt D, Carhart-Harris R, Deco G, Kringelbach M. LSD flattens the hierarchy of directed information flow in fast whole-brain dynamics. Imaging Neuroscience 2025, 3: imag_a_00420. PMID: 40800760, PMCID: PMC12319965, DOI: 10.1162/imag_a_00420.Peer-Reviewed Original Research
2024
Publisher Correction: Heavy-tailed neuronal connectivity arises from Hebbian self-organization
Lynn C, Holmes C, Palmer S. Publisher Correction: Heavy-tailed neuronal connectivity arises from Hebbian self-organization. Nature Physics 2024, 21: 486-486. DOI: 10.1038/s41567-024-02748-x.Commentaries, Editorials and LettersIs stochastic thermodynamics the key to understanding the energy costs of computation?
Wolpert D, Korbel J, Lynn C, Tasnim F, Grochow J, Kardeş G, Aimone J, Balasubramanian V, De Giuli E, Doty D, Freitas N, Marsili M, Ouldridge T, Richa A, Riechers P, Roldán É, Rubenstein B, Toroczkai Z, Paradiso J. Is stochastic thermodynamics the key to understanding the energy costs of computation? Proceedings Of The National Academy Of Sciences Of The United States Of America 2024, 121: e2321112121. PMID: 39471216, PMCID: PMC11551414, DOI: 10.1073/pnas.2321112121.Peer-Reviewed Original ResearchProperties of physical systemsStochastic thermodynamicsThermodynamic propertiesThermodynamics of computationPhysical systemsComputational propertiesEnergy cost of computationThermal equilibriumCost of computationGlobal clockThermodynamicsComputerDigital devicesEnergy costDigital systemsDigital computer